This paper presents a fast lidar-inertial odometry (LIO) system that is robust to aggressive motion. To achieve robust tracking in aggressive motion scenes, we exploit the continuous scanning property of lidar to adaptively divide the full scan into multiple partial scans (named sub-frames) according to the motion intensity. And to avoid the degradation of sub-frames resulting from insufficient constraints, we propose a robust state estimation method based on a tightly-coupled iterated error state Kalman smoother (ESKS) framework. Furthermore, we propose a robocentric voxel map (RC-Vox) to improve the system's efficiency. The RC-Vox allows efficient maintenance of map points and k nearest neighbor (k-NN) queries by mapping local map points into a fixed-size, two-layer 3D array structure. Extensive experiments were conducted on 27 sequences from 4 public datasets and our own dataset. The results show that our system can achieve stable tracking in aggressive motion scenes that cannot be handled by other state-of-the-art methods, while our system can achieve competitive performance with these methods in general scenes. In terms of efficiency, the RC-Vox allows our system to achieve the fastest speed compared with the current advanced LIO systems.

翻译：本文提出一种对剧烈运动鲁棒的快速激光雷达-惯性里程计（LIO）系统。为实现剧烈运动场景下的鲁棒跟踪，我们利用激光雷达的连续扫描特性，根据运动强度自适应地将完整扫描划分为多个局部扫描（称为子帧）。为避免子帧因约束不足导致的性能退化，我们提出基于紧耦合迭代误差状态卡尔曼平滑器（ESKS）框架的鲁棒状态估计方法。此外，我们提出机器人中心体素地图（RC-Vox）以提升系统效率。该地图通过将局部地图点映射至固定大小的双层三维数组结构，实现地图点的高效维护与k近邻（k-NN）查询。我们在4个公开数据集的27个序列及自有数据集上进行了大量实验，结果表明：本系统能在其他现有先进方法无法处理的剧烈运动场景中实现稳定跟踪，同时在常规场景中达到与这些方法相媲美的性能。效率方面，RC-Vox使得本系统相比当前先进LIO系统具有最快处理速度。